In vitro method for predicting whether a compound is genotoxic in vivo

ABSTRACT

The invention is in the field of genomics and it provides an in vitro method for predicting whether a compound is genotoxic in vivo. It provides a method that employs the analysis of expression profiles of primary mouse hepatocytes as an in vitro system to discriminate false GTX compounds from true GTX carcinogens. It was found that differential expression of a number of genes could reliably predict whether a compound was a true genotoxic compound.

FIELD OF THE INVENTION

The invention is in the field of genomics and it provides an in vitro method for predicting whether a compound is genotoxic in vivo.

BACKGROUND OF THE INVENTION

The classic 2 year rodent bioassay is the standard test for identifying the carcinogenic potential of chemical compounds. Such tests are time-consuming and costly. Moreover, they require the sacrifice of many animal lives. In vitro systems are therefore preferred; however, there is no reliable in vitro method for accurately predicting the genotoxicity of a compound in vivo. (1,2).

Well-established in vitro systems frequently used to identify the genotoxic potential of chemical compounds are for instance the bacterial Ames test, the mouse lymphoma assay, the micronucleus test and the chromosomal aberration test (3).

These classic in vitro genotoxicity tests, however, have been shown to generate an extremely high false positive rate when compared to in vivo carcinogenicity data. (3). False positive in this context means that the compound yields a positive result in the in vitro assay whereas it is negative for genotoxicity in an in vivo assay.

Because of the low predictive value of current in vitro assays, a compound that tested positive in an in vitro assay has to be retested in an in vivo assay in order to verify whether the compound is a true genotoxic (GTX) compound. This generates a lot of extra costs and efforts, as well as the sacrifice of many animal lives.

Therefore, new and more predictive in vitro systems are desired in the art which are capable of reliably discriminating genotoxins from non-genotoxins.

SUMMARY OF THE INVENTION

The present invention employs the analysis of expression profiles of primary mouse hepatocytes as an in vitro system to discriminate GTX compounds from non-GTX compounds and also can predict whether a compound found positive in a conventional assay is a false GTX compound or a true GTX compound. It was found that differential expression of a number of genes could reliably predict whether a compound was a true genotoxic compound.

Hence, the invention relates to an in vitro method for distinguishing between genotoxic and non-genotoxic compounds by determining the expression level of at least gene 1700007K13Rik in primary mouse hepatocytes exposed to a potentially genotoxic compound and comparing the expression level thus obtained with a normal value of expression of said at least gene 1700007K13Rik wherein it is concluded that a compound is genotoxic if the expression of said at least gene 1700007K13Rik is increased at least two-fold.

DETAILED DESCRIPTION OF THE INVENTION

Chemical compounds, which are able to cause gene mutations or chromosomal damage in vivo are herein defined as true genotoxins (true GTX) (6, 12). False positive genotoxins (false positive GTX or false GTX) are herein defined as compounds that are not capable of causing gene mutations or chromosomal damage in vivo, but are positive in a conventional in vitro assay for genotoxicity.

Gene mutations or chromosomal damage may occur when the compound covalently binds to DNA in vivo. Such binding to DNA may be not or incorrectly repaired which may lead to mutations accumulating in time and ultimately inducing the formation of tumors (6, 12).

The present invention employs the analysis of expression profiles of primary mouse hepatocytes as an in vitro system to discriminate GTX from non-GTX compounds and also false GTX compounds from true GTX compounds. It was found that differential expression of a number of genes could reliably predict whether a compound was a true genotoxic compound. So as a first step in the method according to the invention, a culture of primary mouse hepatocytes is provided. The skilled person is aware of the various methods that may be used to obtain a culture of primary mouse hepatocytes. The examples provided herein may provide additional guidance. In a further step of the method according to the invention, an assay is provided capable of determining the expression of gene 1700007K13Rik. This gene is also known under its Genebank access code AK005731 or its Entrez Gene ID 69327. Assays that may determine gene expression are also known in the art. Such an assay may consist of an assay capable of determining the expression of a single gene, such as a single PCR-based assay or a hybridization assay. In the alternative, a multiplex assay may be used, consisting of a plurality of different assays that can be performed simultaneously. This allows for the determination of simultaneous expression of more than one gene. Even more advantageously, the assay is a nucleic acid microarray such as a DNA microarray, such as a GeneChip® provided by Affymetrix.

Other genes that may be used in the determination of GTX from non-GTX compounds are listed in Tables 1 and 2. The genes provided in table 1 and 2 are readily accessible and identifiable for a person skilled in the art by their trivial name only. For reason of convenience, also the Genebank accession codes and Entrez Gene ID are given in table 1 and table 2. Primary sequences of these genes are published and can easily be retrieved from numerous public sources, such as Genebank.

TABLE 1 Genes suitable in the method according to the invention GENEBANK ENTREZ Access code GENE SYMBOL GENE ID AK005731 1700007K13Rik 69327 AK010447 Smyd3 69726 BB318221 Zdhhc14 224454 BG261907 Large 16795 Y15910 Diap2 54004 AV095209 Mthfd1l 270685 AK019979 2610528E23Rik 66497 BC016073 Cdkal1 68916 BB821363 Scfd2 212986 AI596632 Ptprg /// LOC632664 19270 AW986246 Maoa 17161 NM_028803 Gbe1 74185 AV141095 1110033M05Rik 68675 AF000969 Cadps2 320405 BB526605 Mipol1 73490 NM_008576 Abcc1 17250 BG070887 Gtdc1 227835 AW543460 Pard3 93742 BC016265 Ube2e2 218793 AV223474 Zdhhc14 224454 AI987929 Ndrg1 17988 AK009736 Gpr137b /// LOC664862 /// LOC673335 AK007766 1810044A24Rik 76510 AK004419 Fbxl17 50758 AV173571 1700106N22Rik 73582 BB308836 Ppm1l 242083 BC004827 Psat1 107272 AW240761 Tbc1d5 72238 BG066903 Kif16b 16558 NM_025770 Atg10 66795 BC025915 Cova1 209224 NM_018770 Igsf4a 54725 AF022072 Grb10 14783 BC025837 Sbk1 104175 BG076151 Ppm1d 53892 BF719766 Thyn1 77862 AV377066 9130221J18Rik 102123 BG065754 Ccng1 12450 BC025501 Aaas 223921 NM_134188 Acot2 171210 NM_021451 Pmaip1 58801 BC026422 Tgm1 21816 BC015270 Hist2h3c2 97114 NM_053168 Trim11 94091 BB027848 4732466D17Rik 212933 AV327248 Zfp365 /// LOC674611 AV219418 Ldhb 16832 BG069873 Gnb1l 13972 AF204959 Cyp3a25 /// LOC622249 56388 NM_030697 Ankrd47 80880 BM198879 Ercc5 22592 AW543723 AK014608 4632434l11Rik 74041 AV298304 Homez 239099 BC012260 Psmf1 228769 NM_013866 Zfp385 29813 AF065917 Lif 16878 AF297615 Ggta1 14594 BB770528 Rai2 24004 BC012247 Dcxr 67880 NM_011316 Saa4 20211 NM_007987 Fas 14102 BI660702 Ell3 269344 BM230508 A030007D23Rik 319530 AI594683 Dmn 233335 NM_011176 St14 19143 BB463610 4632434l11Rik 74041 BC019882 Acaa1b 235674 AK007854 1810053B23Rik 69857 BC010462 BC010462 209588 BB043558 9230114K14Rik 414108 NM_008522 Ltf 17002 NM_012006 Acot1 26897 BB275142 AW456874 218232 BC008626 Icam1 15894 BI651416 Cdc42bpg 240505

TABLE 2 Genes suitable in the method according to the invention GENEBANK ENTREZ Access code GENE SYMBOL GENE ID AK005731 1700007K13Rik 69327 BI651416 Cdc42bpg 240505 NM_008522 Ltf 17002 BB043558 9230114K14Rik 414108 NM_007987 Fas 14102 BC022148 Ces5 234673 BC019882 Acaa1b 235674 BB463610 4632434I11Rik 74041 BM230508 A030007D23Rik 319530 AI594683 Dmn 233335 AV327248 Zfp365 /// LOC674611 BE956581 Cpt1c 78070 NM_011176 St14 19143 BM200015 Hsdl2 72479 BB223872 Bscl2 14705 AF297615 Ggta1 14594 BC027026 Cdkn2c 12580 NM_012006 Acot1 26897 AK014608 4632434I11Rik 74041 BC012247 Dcxr 67880 BC027121 Spbc25 66442 BG797099 Ddit4l 73284 BB743970 BC015286 234669 BF719766 Thyn1 77862 BC027185 2210023G05Rik 72361 AF033112 Siva 30954 BG065754 Ccng1 12450 BB781615 6530418L21Rik 109050 BC013893 Masp2 17175 BC003284 Wdr21 73828 BC006713 Dgka 13139 NM_011075 Abcb1b 18669 BB009155 BG967046 Tbc1d2 381605 NM_030697 Ankrd47 80880 BB275142 AW456874 218232 AV246296 Eda2r 245527 NM_013738 Plek2 27260 NM_018881 Fmo2 55990 BM936480 Fmo2 55990 BM198879 Ercc5 22592 AK018383 Tmem19 67226 AV254764 BC021352 Plod2 26432 BB027848 4732466D17Rik 212933 AK017734 Tmem14a 75712 AF069954 Bscl2 14705 BB770528 Rai2 24004 NM_009897 Ckmt1 12716 AK007854 1810053B23Rik 69857 BI966443 Itm2a 16431 NM_013929 Siva 30954 BG076151 Ppm1d 53892 AV251625 Ddit4l 73284 AV219418 Ldhb 16832 NM_011316 Saa4 20211 NM_007980 Fabp2 14079 BB046347 Mycbp 56309 AF335325 Ddit4l 73284 AK010738 Ascl2 17173 NM_134188 Acot2 171210 NM_008935 Prom1 19126 BB140436 Slc16a10 72472 NM_019738 Nupr1 56312 X62701 Plaur 18793 AV141095 1110033M05Rik 68675 AI747296 Gmds 218138 BC005552 Asns 27053 BB458460 Chchd6 66098 BG076333 Mthfd2 17768 AK019979 2610528E23Rik 66497 AV095209 Mthfd1l 270685 AV216768 Phgdh /// LOC668771 /// LOC671972 /// LOC673015 AV221299 Gfra1 14585 BQ174991 Chsy1 269941 NM_013642 Dusp1 19252 L21027 Phgdh /// LOC666422 /// LOC666875 /// 236539 LOC669985 /// LOC671102 /// LOC673015 /// LOC675010 BB204486 Phgdh /// LOC382931 /// LOC384524 /// LOC385344 /// LOC547171 /// LOC627427 /// LOC666422 /// LOC6 BC025169 Chac1 69065 BC026131 Slc7a5 20539 BC010318 Pck2 74551 BB730977 Cachd1 320508 AA561726 Phgdh /// LOC668771 /// LOC670155 /// LOC671972 /// LOC673015 BC012955 Trib3 228775 BC004827 Psat1 107272 NM_007556 Bmp6 12161 NM_134147 D930010J01Rik 107227 AV173869 D14Ertd171e 238988 AF022072 Grb10 14783 BC019379 Gprk5 14773 AK010447 Smyd3 69726 BC017615 Slc24a3 94249 BB246912 1700112E06Rik 76633 AF000969 Cadps2 320405 BG066491 Fhod3 225288 AF055573 Fhit 14198 NM_053122 Immp2l 93757

In another step of the method according to the invention, the compound to be tested is contacted with the primary mouse hepatocytes. The skilled person will be aware of the metes and bounds of this step. In the examples section, the concentrations used for 10 true and false GTX compounds are provided as guidance. In general, the use of cytotoxic concentrations should be avoided. The skilled person will know how to avoid using cytotoxic concentrations of test compounds.

It was found to be useful to measure the gene expression in primary mouse hepatocytes at two consecutive moments in time or at two different intervals. These moments should be chosen empirically depending on a suitable expression pattern of the gene 1700007K13Rik or the genes listed in table 1 and 2 in the particular primary mouse hepatocytes chosen for the method. In general however, intervals of 1 to 2 days were found most appropriate. In the particular examples shown, it was chosen to analyse the gene expression at 24 hours and 48 hours after contacting the mouse hepatocytes with the test compound. This was found to produce very satisfying results.

In order to obtain reproducible results, it was found advantageous to obtain at least three independent readings of the gene expression. Hence, the above steps may be repeated at least twice to obtain a more reproducible and reliable result.

The results of the gene expression analysis may then be fed into a computer program capable of performing a supervised classification analysis. This method was found to provide superior results as compared to unsupervised classification methods and hierarchical clustering methods.

Supervised learning methods are computational approaches for class prediction based on biological data, such as generated with microarrays. Several methods have been shown to perform well with microarray data. Examples are support vector machines (SVM), RandomForest (RF), k-nearest neighbours (KNN), diagonal linear discriminant analysis (DLDA), shrunken centroids (PAM), classification and regression trees (CART), probabilistic neural network (PNN) and Weighted Voting (WV). The shrunken centroids software (Prediction Analysis of Microarray, PAM, Version 2.1 (Sep. 14, 2005), http://www-stat.stanford.edu/˜tibs/PAM/; Tibshirani et al. PNAS 2002 99:6567-6572) was used in this invention for identifying genes.

Such computer programs may be trained with a data set obtained for known true GTX and false GTX compounds at the intervals chosen, such as presented in Tables 5-8. These programs, when trained with a suitable data set, can be used to predict whether a compound is a GTX compound or a non-GTX compound or for distinguishing false GTX from true GTX. This is based on a computational comparison of expression of said at least one gene with and without the test compound at the two consecutive moment in time with data from reference compounds (e.g. provided in Table 5-8) by means of a supervised classification method.

By repeating the steps of treating cells with the compound at least 2 times to obtain at least three measurements, at least six independent preliminary predictions can be obtained for the genotoxicity of a test compound; 3 repeats at two consecutive moments in time. These data may then be converted into a final prediction for the genotoxicity of a test compound by using the algorithm provided in table 3.

TABLE 3 Prediction Prediction at first at second time point time point Final prediction 3 repeats False 3 repeats False False-positive genotoxic = Not positive positive Genotoxic in vivo 3 repeats False 2 repeats False False-positive genotoxic = Not positive positive Genotoxic in vivo 3 repeats False 1 repeats False Equivocal = no prediction possible positive positive yet 3 repeats False 0 repeats False True genotoxic = Genotoxic in vivo positive positive 2 repeats False 3 repeats False False-positive genotoxic = Not positive positive Genotoxic in vivo 2 repeats False 2 repeats False False-positive genotoxic = Not positive positive Genotoxic in vivo 2 repeats False 1 repeats False Equivocal = no prediction possible positive positive yet 2 repeats False 0 repeats False True genotoxic = Genotoxic in vivo positive positive 1 repeats False 3 repeats False Equivocal = no prediction possible positive positive yet 1 repeats False 2 repeats False Equivocal = no prediction possible positive positive yet 1 repeats False 1 repeats False True genotoxic = Genotoxic in vivo positive positive 1 repeats False 0 repeats False True genotoxic = Genotoxic in vivo positive positive 0 repeats False 3 repeats False True genotoxic = Genotoxic in vivo positive positive 0 repeats False 2 repeats False True genotoxic = Genotoxic in vivo positive positive 0 repeats False 1 repeats False True genotoxic = Genotoxic in vivo positive positive 0 repeats False 0 repeats False True genotoxic = Genotoxic in vivo positive positive

Hence, the invention relates to a method for distinguishing between genotoxic and non-genotoxic compounds by determining the expression level of at least gene 1700007K13Rik in primary mouse hepatocytes exposed to a potentially genotoxic compound and comparing the expression level thus obtained with a normal value of expression of said at least gene 1700007K13Rik wherein it is concluded that a compound is genotoxic if the expression of said at least gene 1700007K13Rik is increased at least two-fold.

The method according to the invention may even be improved by analyzing more than one gene. Preferably the method employs the detection of the expression level of at least one additional gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284). Also the method may be improved by adding at least one additional gene selected from the group consisting of the genes provided in table 1 and table 2, such as 2 genes or more than 2, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or even 30 or more genes selected from the genes provided in table 1 and table 2.

In a method according to the invention, at least 3 measurements of expression of said at least one gene with and without the test compound at the two consecutive moments in time are compared with data obtained from known true genotoxic compounds. The true GTX compounds used in the study as presented here are known genotoxic compounds as well as known carcinogens. They exhibit a positive result in several in vitro assays as well as several in vivo models (Table 4). They are known to induce genotoxicity in vivo.

The false GTX compounds used in the present study are known non-genotoxic compounds in vivo as well as non-carcinogens in vivo. They only show a (false) positive result in certain in vitro tests, but are known not to induce genotoxicity in vivo. These compounds are also listed in table 4.

The non-genotoxic carcinogens used in the study (Table 4) are known carcinogens in vivo, but do not induce genotoxicity in vivo, nor with in vitro tests. The non-carcinogens used in the study are not known as carcinogens and do not cause genotoxicity in vivo, nor with in vitro tests.

TABLE 4 Overview of the compounds used in primary mouse hepatocyte exposure Abbre- Concen- Chemical viation CAS nr. tration Vehicle True GTX compounds Benzo(a)pyrene BaP 50-32-8  30 μM DMSO Aflatoxin B1 AFB1 1162-65-8  10 μM DMSO 2-Acetylaminofluorene 2-AAF 53-96-3 125 μM DMSO Dimethylnitrosamine DMN 62-75-9  5 μM PBS Mitomycin C MitC 50-07-7  5 μM PBS Para-Cresidine pCres 120-71-8  8 mM DMSO False GTX compounds o-Anthranilic acid ANAC 118-92-3  2 mM DMSO 2-(Chloromethyl)- 2-CP 6959-47-3 125 μM DMSO pyridine.HCl 4-Nitro-o-phenylene- 4-NP 99-56-9  2 mM DMSO diamine Quercetin Q 117-39-5 200 μM DMSO 8-Hydroxyquinoline 8-HQ 148-24-3 150 μM Ethanol Non-genotoxic carcinogens Cyclosporin A CsA 59865-13-3  10 μM DMSO 2,3,7,8-Tetrachloro- TCDD 1746-01-6 200 nM DMSO dibenzodioxin Carbon tetrachloride CCI4 56-23-5  1 mM DMSO Tetradecanoylphorbol TPA 16561-29-8  1 μM DMSO Acetate Wy-14,643 Wyeth 50892-23-4 300 μM DMSO Non-genotoxic Non-carcinogens Bisphenol A BPA 80-05-7 100 μM DMSO Diclofenac DF 15307-86-5 300 μM DMSO Bis(tri-n-butyltin)oxide TBTO 56-35-9 300 nM Ethanol Amiodarone AMD 1951-25-3  10 μM DMSO D-Mannitol dMan 69-65-8  2 mM DMSO

Table 4a provides the log 2-gene expression ratios.

TABLE 4a Ratios of gene expression treated/untreated 1700007K13RIK GAS2L3 SPC25 DDIT4L genotoxic carcinogens BaP 2.46394 1.19836 1.28371 2.06696 AFB 2.91154 1.23443 1.52318 1.61622 DMN 2.14075 0.21656 0.83678 0.76622 MMC 4.95769 1.55118 2.1958 2.7547 pCres 0.20861 1.56162 1.33141 1.06569 Nongenotoxic carcinogens CsA −0.0019 −0.4304 0.19765 0.64756 TCDD 0.0752 0.21999 0.01802 0.26458 CCI4 0.39061 −0.3188 0.35438 0.48935 TPA −0.0617 0.01747 0.03809 0.05594 Wyeth −0.3527 −0.7113 −0.0756 −0.1013 Noncarcinogens BPA −0.0382 −0.06 −0.0457 0.0293 DF 0.09622 −0.2839 0.40792 0.12681 TBTO 0.09131 −0.0161 0.21862 0.31897 AMD −0.2612 0.27578 −0.4118 −0.309 dMan 0.02382 −0.2175 −0.0363 0.03421

Hepatocyte cells were incubated and exposed to a compound for 24 h before being harvested for RNA isolation. In order to get reproducible data, four independent replicate biological experiments with compound-exposed hepatocytes from different mice were conducted for each compound and gene expression data were compared to four independent replicate biological experiments with control- or vehicle-exposed hepatocytes from different mice.

The results of the gene expression analysis may then be fed into a statistical software package such as R, Splus, or Microsoft Excel. For genes that are able to discriminate between GTX and non-GTX compounds, differential expression can be scored on a gene-by-gene basis. We found it advantageous to use the following scoring system: A point was scored if two criteria were met, (a) if the gene expression values for the four compound-exposed samples differed significantly from the vehicle-exposed samples with a t-test p-value <0.01; (b) if the average gene expression value for the four compound-exposed samples was at least twice that of the average vehicle-exposed samples. If none or only one of these criteria were met, no point was scored.

After the discriminating genes are scored, the statistical software can be used to compare the total number of positive genes between genotoxic and non-genotoxic compounds and apply a suitable threshold to discriminate between classes, Using said four genes mentioned in the table (1700007K13RIK, GAS2L3, SPC25, DDIT4L), we found that if this score is 0, a compound can be believed not to be a genotoxic carcinogen. If this score is 1, 2, 3 or 4, a compound is genotoxic.

For each of the four genes mentioned in the table (1700007K13RIK, GAS2L3, SPC25, DDIT4L), a statistical comparison was made between the normalized expression data for these genes. A point was scored if two criteria were met, (a) if the gene expression values for the four compound-exposed samples differed significantly from the vehicle-exposed samples with a t-test p-value <0.01; (b) if the average gene expression value for the four compound-exposed samples was at least twice that of the average vehicle-exposed samples. If none or only one of these criteria were met, no point was scored.

For each compound, the sum of the scores for four genes is taken. If this score is 0, a compound can be believed not to be genotoxic. If this score is 1, 2, 3 or 4, a compound is genotoxic.

TABLE 4b scores for each of the four genes Total 1700007K13RIK GAS2L3 SPC25 DDIT4L score BaP average 1 1 1 1 4 AFB average 1 1 1 1 4 DMN 1 0 0 0 1 average MMC 1 1 1 1 4 average pCres 0 1 1 1 3 average CsA average 0 0 0 0 0 TCDD 0 0 0 0 0 average CCI4 0 0 0 0 0 average TPA average 0 0 0 0 0 Wyeth 0 0 0 0 0 average BPA average 0 0 0 0 0 DF average 0 0 0 0 0 TBTO 0 0 0 0 0 average AMD 0 0 0 0 0 average dMan 0 0 0 0 0 average

For the combination of the particular mouse hepatocytes and time intervals chosen in the study exemplified in the examples, the data obtained with the true GTX compounds at 24 and 48 hours are provided in table 5 and table 6 respectively. The corresponding data obtained with the false GTX compounds is provided in table 7 and table 8 respectively.

TABLE 5 Gene expression data at 24 hr with true GTX compounds. GENE- BANK AFB1 BaP 2-AAF DMN MitC ACCESS 24 h 24 h 24 h 24 h 24 h CODE average average average average average AK010447 −1.2553 −1.0413 −0.17513 −1.0358 −1.43853 BB318221 −1.0808 −1.67917 −0.41097 −1.04093 −2.70633 BG261907 −1.11937 −1.3429 −0.25017 −1.3871 −3.87647 Y15910 −1.88907 −1.35203 −0.36603 −1.63633 −2.81277 AV095209 −0.83977 −0.58853 0.4403 −1.00697 −2.45413 AK019979 −2.53743 −0.6154 0.324767 −1.32373 −3.31723 BC016073 −1.09967 −0.6631 −0.1464 −1.24277 −1.96257 BB821363 −1.35393 −1.40327 −0.1216 −1.143 −2.48487 AI596632 −1.9945 −1.8362 −0.0981 −1.90023 −4.79467 AW986246 −0.33847 0.1617 0.018533 −0.37203 −0.21193 NM_028803 −1.00427 −0.54163 −0.2716 −1.10333 −1.8898 AV141095 −1.29513 −1.05753 −0.28807 −1.37477 −2.28737 AF000969 −2.16997 −1.90953 −0.53713 −1.03907 −2.40647 BB526605 −0.7181 −0.7419 0.346567 −1.5816 −2.03513 NM_008576 −0.18853 0.1024 0.332067 −0.92953 −1.31177 BG070887 −1.47347 −0.97337 −0.01607 −1.11183 −2.35577 AW543460 −0.2756 −0.61937 −0.3156 −1.4802 −3.16427 BC016265 −0.87973 −0.75103 −0.39843 −1.28743 −2.20177 AV223474 −1.02503 −1.37517 −0.44587 −0.9067 −2.28283 AI987929 −0.8074 −0.36477 0.8432 −0.98383 −3.32443 AK009736 −0.50757 0.217433 −0.00723 −0.83673 −1.05647 AK007766 −1.55373 −1.28833 0.010033 −0.8706 −2.22737 AK004419 −1.36467 −1.00123 −0.16797 −1.22027 −4.0931 AV173571 −1.23913 −1.12323 −0.04133 −1.0106 −2.28717 BB308836 −1.07683 −0.61223 −0.14993 −0.98813 −1.64007 BC004827 −1.0032 −0.04097 0.279733 −1.51817 −2.4733 AW240761 −1.11773 −0.703 −0.0924 −1.19117 −2.64317 BG066903 −0.50743 −0.13297 −0.21247 −0.86817 −1.04667 NM_025770 −1.12757 −1.05323 0.016633 −0.51127 −1.32903 BC025915 −0.64197 −0.53963 −0.15683 −0.95017 −1.61897 NM_018770 −0.43897 −0.8982 −0.20653 −0.84117 −2.05413 AF022072 −1.2526 −0.28443 0.5527 −1.91293 −3.6802 BC025837 0.469433 0.380233 −0.2976 0.9087 1.652233 BG076151 0.602567 0.727967 −0.12193 1.946333 2.237733 BF719766 0.389167 0.9747 −0.0442 1.823667 2.045133 AV377066 1.0883 0.757233 −0.65863 0.960533 2.462133 BG065754 0.838267 0.8607 0.0569 0.958667 1.179133 BC025501 1.140267 1.5389 −0.1475 1.6386 2.022933 NM_134188 0.012067 0.5856 0.596967 −0.50007 −0.06593 NM_021451 1.100433 0.6364 −0.7054 3.698367 2.681033 BC026422 0.8843 0.6019 −0.16643 2.220467 2.651367 BC015270 1.092667 0.790767 −0.13273 0.981567 0.633867 NM_053168 1.268333 0.823033 −0.26757 1.0583 1.4821 BB027848 −0.016 0.0761 −0.252 1.126333 2.179833 AV327248 0.805533 1.102433 0.0668 4.114233 4.258133 AV219418 0.281667 0.278133 −0.36947 2.743333 2.872767 BG069873 0.727567 1.299367 −0.27473 2.2987 2.174733 AF204959 1.403933 0.3057 0.2152 −0.29927 0.8169 NM_030697 1.8143 1.4578 −0.25197 3.210733 3.874367 BM198879 1.610067 1.054033 −0.2382 1.2634 2.011933 AW543723 1.426867 1.993167 0.106467 1.950433 2.3145 AK014608 1.764733 1.664767 −0.09823 1.756333 2.3711 AV298304 1.030433 0.6204 −0.18307 1.8715 1.9706 BC012260 1.2375 0.312733 −0.64603 1.0548 2.236967 NM_013866 0.951267 0.776033 −0.13603 2.361133 2.300333 AF065917 0.689633 0.922733 −0.12417 1.803833 1.033367 AF297615 1.1224 1.734833 0.031867 3.2188 2.358333 BB770528 0.7477 0.697733 −0.5052 2.799567 2.603367 BC012247 0.367667 0.445067 −0.08253 1.6567 2.093633 NM_011316 0.2733 0.187133 −0.3157 2.1585 2.611367 NM_007987 0.627967 0.828667 0.069233 2.420733 2.6645 BI660702 0.4068 1.3867 0.1219 2.513867 3.741633 BM230508 0.591867 1.297 0.282033 2.303567 2.556067 AI594683 1.047633 0.731 −0.24517 3.163767 3.670633 NM_011176 1.288967 1.507833 −0.03387 1.574367 2.077667 BB463610 1.7658 2.143967 −0.37777 1.9965 2.535 BC019882 0.4654 2.0133 1.3218 2.057333 3.446367 AK007854 1.391233 0.3514 −0.90083 2.172867 2.898333 BC010462 0.998467 0.666267 −0.14753 0.751867 1.162867 BB043558 1.671433 1.246667 −0.12047 2.406733 2.307633 NM_008522 1.054933 1.113967 0.127667 3.616333 3.9908 NM_012006 1.250567 3.254667 1.9403 2.032633 3.958567 BB275142 1.009433 1.003367 −0.02303 1.902233 1.944367 BC008626 1.4158 0.823433 −0.63527 2.035033 2.3005 BI651416 1.8011 1.678867 0.155167 1.7218 2.482133 AK005731 2.2462 2.015867 0.082767 5.486167 5.7645

TABLE 6 Gene expression data at 24 hr with false GTX compounds GENEBANK 2-CP 4-NP ANAC Q 8Q ACCESS 24 h 24 h 24 h 24 h 24 h CODE average average average average average AK010447 0.060966667 0.496666667 −0.121066667 0.0701 0.076966667 BB318221 −0.2464 0.0942 0.000233333 −0.147933333 0.113766667 BG261907 −0.117733333 0.3643 0.092933333 0.3964 −0.1486 Y15910 0.066933333 −0.539833333 0.065333333 −0.310033333 −0.2692 AV095209 0.5754 0.907233333 0.316166667 0.8331 0.156433333 AK019979 0.3731 0.938366667 0.113866667 0.250166667 0.084466667 BC016073 0.030166667 0.362733333 0.0051 0.199266667 −0.051833333 BB821363 −0.188333333 −0.218966667 −0.084 −0.233766667 0.1765 AI596632 0.225066667 −0.496166667 0.1214 −0.456033333 −0.226966667 AW986246 1.210366667 1.7055 0.016833333 1.388133333 0.319833333 NM_028803 0.524233333 0.363333333 −0.0826 −0.250933333 −0.045933333 AV141095 0.181666667 0.178933333 −0.129333333 0.285466667 −0.070466667 AF000969 −0.100266667 −0.683866667 −0.12 −0.575266667 −0.371733333 BB526605 −0.213366667 1.641133333 0.340733333 0.096166667 −0.014533333 NM_008576 1.199266667 1.696333333 0.358566667 1.2925 0.106 BG070887 −0.067733333 0.343166667 −0.051633333 −0.2209 −0.042733333 AW543460 0.226333333 0.912666667 0.060366667 0.243666667 −0.0366 BC016265 0.0177 0.034333333 0.015066667 0.162166667 −0.254666667 AV223474 −0.409433333 −0.084366667 −0.073466667 −0.2496 −0.069566667 AI987929 −0.100433333 2.650333333 0.6977 1.662633333 0.441433333 AK009736 0.9644 1.582066667 −0.0957 1.170733333 0.533733333 AK007766 −0.123533333 −0.169666667 −0.051666667 −0.044633333 −0.0226 AK004419 −0.120266667 −0.0775 −0.017933333 −0.015366667 0.011133333 AV173571 −0.013666667 −0.003133333 −0.157433333 −0.248433333 0.041966667 BB308836 −0.369233333 0.037633333 −0.0317 0.1666 0.2558 BC004827 0.365366667 0.539466667 0.393733333 0.9258 −0.101133333 AW240761 0.028033333 0.026966667 0.001666667 −0.157033333 −0.200633333 BG066903 0.336833333 1.004933333 0.0241 0.250566667 −0.1277 NM_025770 0.344466667 1.050666667 −0.012633333 0.266333333 −0.1372 BC025915 0.058266667 0.3343 −0.053833333 0.317566667 −0.026433333 NM_018770 0.139966667 0.386766667 −0.088533333 0.468866667 0.1391 AF022072 0.621 0.848566667 0.4544 0.3385 −0.4275 BC025837 −0.504233333 −0.818133333 −0.3033 −0.3383 −0.042266667 BG076151 0.121266667 −0.2933 −0.086366667 −0.136733333 0.072433333 BF719766 0.0395 −0.2859 −0.115266667 0.159566667 −0.193466667 AV377066 −0.6354 −1.694833333 −0.2482 −0.4922 −0.321166667 BG065754 0.3287 −0.250333333 −0.089166667 0.482866667 −0.2279 BC025501 0.0663 −0.2167 −0.226066667 0.4589 0.197866667 NM_134188 0.501033333 −0.763733333 0.869566667 0.127533333 −0.2881 NM_021451 0.023566667 −1.505 0.013033333 −0.611033333 −0.529466667 BC026422 −0.3825 −0.8176 0.0232 0.280033333 0.287266667 BC015270 −0.230966667 −0.7846 0.118566667 −0.392366667 −0.033533333 NM_053168 −0.067833333 −0.1598 −0.201 −0.022566667 −0.0493 BB027848 −0.571633333 −1.7858 −0.036966667 −1.303666667 −0.280733333 AV327248 0.147666667 −0.089166667 0.0212 0.311566667 −0.082 AV219418 −0.796766667 −1.032 −0.2907 −0.180833333 0.0363 BG069873 −0.1027 −0.132566667 −0.210733333 0.007866667 −0.047633333 AF204959 −0.5019 −2.025133333 −0.280033333 −1.232033333 −0.417833333 NM_030697 −0.578533333 0.181633333 −0.162333333 1.3142 0.220433333 BM198879 0.293533333 −0.328833333 −0.2194 0.1402 −0.006766667 AW543723 0.148666667 0.670633333 0.294266667 0.419266667 −0.0994 AK014608 0.5726 0.295766667 −0.176833333 0.032733333 −0.122533333 AV298304 0.1881 −0.583933333 −0.0861 0.2496 −0.036066667 BC012260 −0.824033333 0.118933333 −0.1838 −0.168866667 0.254466667 NM_013866 −0.1346 −0.280766667 −0.195333333 0.065433333 0.058933333 AF065917 0.012133333 −0.457266667 −0.202333333 −0.102433333 −0.210133333 AF297615 0.069633333 −0.279733333 0.031033333 0.804666667 −0.0684 BB770528 −0.327066667 −1.122266667 −0.083266667 −0.310933333 0.138566667 BC012247 −0.326066667 −1.499833333 −0.0311 −0.2845 −0.0122 NM_011316 −0.044633333 −0.590933333 −0.1988 −0.131366667 0.0831 NM_007987 0.231266667 −1.231166667 −0.133733333 0.174366667 −0.055233333 BI660702 0.081666667 −0.608966667 −0.153866667 −0.058966667 0.051133333 BM230508 0.3615 −0.6065 −0.1178 0.3449 0.034966667 AI594683 −0.315666667 −0.351733333 −0.216266667 −0.2712 −0.097833333 NM_011176 −0.053933333 −0.571666667 −0.144533333 0.591366667 0.057833333 BB463610 0.384833333 0.210633333 −0.3672 −0.1026 −0.2188 BC019882 −0.0399 −1.259166667 1.0984 0.712366667 0.0605 AK007854 −0.4492 −1.735733333 −0.428266667 −0.9336 −0.076 BC010462 −0.589533333 −0.865433333 −0.408866667 −0.5852 0.056633333 BB043558 0.494633333 −0.415933333 −0.129166667 0.343133333 −0.373433333 NM_008522 −0.358133333 −0.301766667 0.0771 −0.095933333 −0.059633333 NM_012006 0.6012 −1.4501 1.861966667 1.0762 −0.253166667 BB275142 −0.195366667 −0.418433333 0.028233333 −0.500433333 −0.025066667 BC008626 −0.242 −2.247633333 −0.270533333 −0.372466667 −0.4422 BI651416 0.052366667 −0.0339 0.013866667 0.265266667 0.136366667 AK005731 0.196466667 −0.511133333 −0.173333333 0.6919 0.114266667

TABLE 7 Gene expression data at 48 hr with true GTX compounds GENEBANK AFB1 BaP 2-AAF DMN MitC ACCESS 48 h 48 h 48 h 48 h 48 h CODE average average average average average AK005731 2.2191 3.0498 0.011133333 4.616833333 6.088666667 BI651416 1.978966667 1.9573 0.350166667 1.8588 2.2069 NM_008522 2.271133333 2.9351 0.1967 4.993466667 5.9249 BB043558 1.629366667 1.692466667 0.195966667 2.186833333 2.331833333 NM_007987 0.877166667 1.352666667 0.515966667 1.665166667 2.2406 BC022148 1.0426 1.323566667 0.456033333 1.8449 2.668233333 BC019882 1.2329 2.160033333 1.576933333 1.494 2.441633333 BB463610 1.2472 1.672366667 0.0872 1.546 2.727766667 BM230508 0.637 1.158366667 0.0745 1.539833333 2.570533333 AI594683 1.508466667 1.465666667 −0.052133333 3.7761 4.611033333 AV327248 1.4639 2.067166667 −0.091766667 4.022733333 4.918266667 BE956581 1.7703 1.451433333 0.1808 3.032866667 3.679666667 NM_011176 1.3047 1.4287 −0.263233333 1.597133333 1.856466667 BM200015 0.934166667 1.0174 0.512666667 1.160333333 1.702233333 BB223872 0.526333333 1.1634 0.321566667 1.694233333 2.200766667 AF297615 2.175266667 1.571833333 −0.679133333 3.153166667 1.840933333 BC027026 0.6995 1.378666667 0.806333333 2.570766667 2.873 NM_012006 1.7541 2.867133333 2.770866667 1.6671 3.166133333 AK014608 1.185766667 1.245366667 −0.019866667 1.390533333 2.8746 BC012247 0.7954 1.476733333 0.6286 1.669566667 2.2674 BC027121 0.979866667 1.345266667 −0.134133333 2.074533333 2.741666667 BG797099 1.027266667 1.8809 −0.061733333 1.703866667 2.6244 BB743970 0.381333333 1.510133333 0.766433333 3.154366667 3.5588 BF719766 1.1094 0.950666667 0.265166667 1.2016 2.112066667 BC027185 0.0776 0.708133333 0.4588 1.461733333 2.115066667 AF033112 1.032833333 1.4254 −0.039466667 1.816933333 2.218866667 BG065754 0.688433333 0.974633333 0.373833333 0.995333333 1.3079 BB781615 0.972233333 0.927133333 −0.116433333 1.486966667 0.895166667 BC013893 0.3479 1.040066667 0.493633333 0.548733333 1.863933333 BC003284 0.703633333 0.731333333 0.025633333 1.379133333 2.092633333 BC006713 0.941966667 0.4721 0.4411 1.668066667 1.2958 NM_011075 1.1557 1.197733333 −0.5522 2.401 3.096466667 BB009155 1.3686 0.784 −0.5851 2.342966667 2.9201 BG967046 0.624866667 0.593566667 0.0391 1.1973 1.305133333 NM_030697 1.270633333 1.3687 −0.478333333 2.5583 3.958066667 BB275142 0.5302 1.0559 0.110166667 1.161633333 2.281633333 AV246296 1.097933333 1.1524 −0.54 0.957233333 1.4788 NM_013738 0.4146 0.9012 −0.239566667 1.562466667 2.749033333 NM_018881 0.200133333 3.369333333 0.906066667 0.948066667 2.279366667 BM936480 0.1463 3.0213 0.916866667 0.778066667 2.006033333 BM198879 1.579166667 0.808866667 −0.288733333 1.096266667 1.927533333 AK018383 0.406933333 1.273266667 0.230266667 0.824633333 0.994 AV254764 1.542533333 1.480466667 −0.006466667 1.133466667 1.411166667 BC021352 1.590266667 0.705233333 −1.126533333 3.512033333 3.121333333 BB027848 −0.004366667 0.473766667 0.5378 1.190666667 1.879 AK017734 0.4796 0.7529 0.0451 1.0611 1.834766667 AF069954 0.2361 0.8863 0.351766667 1.631166667 2.259766667 BB770528 0.867766667 0.7877 −0.5594 1.709333333 2.0332 NM_009897 1.053566667 1.182233333 −0.003733333 3.618966667 4.658266667 AK007854 1.5416 1.6125 1.4303 0.839633333 1.500233333 BI966443 0.698966667 1.3186 0.1201 3.294933333 4.094033333 NM_013929 0.744866667 1.061633333 −0.041633333 1.890866667 2.481333333 BG076151 0.477866667 0.759933333 −0.3358 1.205166667 2.5119 AV251625 0.547133333 1.730566667 0.071033333 1.246066667 2.476566667 AV219418 0.8603 1.558433333 0.783933333 4.5473 4.167766667 NM_011316 0.6494 0.873466667 0.4369 2.0898 2.255733333 NM_007980 0.411966667 1.752833333 1.503166667 0.325166667 2.9391 BB046347 0.5079 0.7766 0.217766667 0.774333333 1.349533333 AF335325 1.3645 1.4875 −0.308833333 1.6857 1.754566667 AK010738 0.6579 0.911166667 0.1851 1.342566667 1.979733333 NM_134188 −0.158966667 0.288366667 0.908766667 −0.637766667 0.4786 NM_008935 −1.772 −1.4799 0.027833333 −1.382066667 −3.600866667 BB140436 −0.4938 −0.457233333 0.469333333 −1.281166667 −1.601366667 NM_019738 −2.369733333 −2.585566667 −0.193033333 −2.9169 −2.771766667 X62701 −0.661133333 −1.2338 −0.356033333 0.071566667 −1.3159 AV141095 −1.192433333 −1.4056 −0.582933333 −0.866333333 −2.411233333 AI747296 −1.533166667 −2.124066667 −0.500266667 −0.433266667 −2.056266667 BC005552 −0.7309 −0.212133333 −0.125966667 −0.523533333 −1.341933333 BB458460 −1.131533333 −1.384133333 −0.336766667 −0.693433333 −2.7207 BG076333 −0.3573 −0.409133333 −0.130566667 −0.799466667 −1.870033333 AK019979 −2.270633333 −1.0966 −0.509866667 −1.110233333 −3.757366667 AV095209 −0.6169 −0.765433333 −0.371833333 −0.453966667 −2.780333333 AV216768 −1.281733333 −1.446966667 −0.6814 −0.2641 −4.148766667 AV221299 −0.787333333 −1.168933333 0.6759 −1.5693 −3.709666667 BQ174991 −0.6262 −1.407933333 −0.342633333 −0.28 −2.777066667 NM_013642 −0.252766667 −1.442166667 −0.653466667 0.3461 −0.784533333 L21027 −1.867666667 −1.779266667 −0.728966667 −0.4472 −4.611766667 BB204486 −1.371333333 −1.5693 −0.6445 −0.3108 −4.143533333 BC025169 −1.029533333 −0.511366667 −0.222233333 −1.295533333 −3.296166667 BC026131 −0.447566667 −1.045333333 −0.3489 −0.7571 −0.998633333 BC010318 −0.956533333 −0.552866667 −0.022133333 −1.166866667 −1.728666667 BB730977 −0.256 0.421766667 −0.993666667 −0.656 −2.360033333 AA561726 −1.480766667 −1.7432 −0.7419 −0.353433333 −4.371633333 BC012955 −1.402833333 −0.944466667 0.050733333 −1.970133333 −2.240533333 BC004827 −1.254533333 −1.186033333 −0.2015 −1.0689 −3.638633333 NM_007556 −0.9667 −1.089 0.027033333 −1.289533333 −4.012333333 NM_134147 −1.510133333 −1.2396 0.042133333 −1.579366667 −2.8471 AV173869 −0.881433333 −1.923533333 −0.3782 −1.271666667 −2.2889 AF022072 −0.991333333 −1.017566667 0.0482 −1.2797 −3.369966667 BC019379 −1.263466667 −1.9807 −1.214833333 −0.360166667 −2.7493 AK010447 −1.2768 −1.207066667 −0.3256 −0.863433333 −1.6388 BC017615 −2.585433333 −2.233833333 −0.522933333 −2.7334 −3.946966667 BB246912 −1.2439 −1.539866667 −0.226366667 −0.980566667 −1.866433333 AF000969 −2.345533333 −2.251433333 −0.831866667 −1.113833333 −3.3606 BG066491 −1.6995 −2.097833333 −1.541033333 −0.975666667 −1.928533333 AF055573 −2.3744 −1.5573 −0.289466667 −1.912533333 −2.915766667 NM_053122 −2.304433333 −1.795033333 −0.105966667 −2.3248 −4.049166667

TABLE 8 Gene expression data at 48 hr with false GTX compounds GENEBANK 2-CP 4-NP ANAC Q 8Q ACCESS 48 h 48 h 48 h 48 h 48 h CODE average average average average average AK005731 −0.6019 −0.9581 −0.649766667 −0.8953 −0.039966667 BI651416 −0.087533333 −0.028733333 −0.105066667 −0.498166667 0.067766667 NM_008522 0.0213 −0.239266667 −0.069566667 −0.039166667 0.0486 BB043558 −0.229266667 −0.273633333 −0.188 0.038166667 −0.234666667 NM_007987 −0.3447 −0.8469 −0.005833333 −0.073433333 0.034866667 BC022148 0.191933333 −1.1322 −0.1976 0.2323 0.044966667 BC019882 0.814833333 −2.4942 0.194833333 −0.021766667 −0.171866667 BB463610 0.0113 0.037266667 −0.218566667 −0.254 −0.236866667 BM230508 −0.2554 −0.973133333 −0.470966667 −0.670733333 0.076033333 AI594683 −0.2197 −0.4763 −0.522366667 −0.1952 0.123533333 AV327248 −0.221533333 −0.014933333 0.0856 −0.184166667 0.117633333 BE956581 0.0444 0.0346 0.042666667 0.106333333 0.403033333 NM_011176 −0.0046 −1.635266667 −0.460666667 −0.357866667 0.1358 BM200015 −0.3607 −0.4429 0.009533333 −0.2469 0.080866667 BB223872 0.174333333 −0.653566667 −0.428733333 −0.240866667 0.057533333 AF297615 −0.5906 −1.352433333 0.111 −0.029433333 −0.009333333 BC027026 −0.217666667 0.052733333 −0.007933333 0.363633333 0.2479 NM_012006 −0.1277 −0.006733333 1.5971 0.737333333 0.580566667 AK014608 −0.012633333 −0.307233333 −0.455 −0.318133333 −0.2447 BC012247 0.522066667 −0.635466667 −0.5319 0.185266667 −0.006933333 BC027121 −0.6553 −0.2406 −0.1424 0.003766667 −0.1207 BG797099 0.004233333 −0.066966667 −0.020966667 −0.318533333 0.0658 BB743970 −0.168566667 −0.1636 0.155133333 −0.154266667 0.1936 BF719766 −0.125866667 −0.5024 0.076366667 0.168333333 −0.271666667 BC027185 0.062733333 −1.381733333 −0.238133333 −0.431666667 −0.120933333 AF033112 −0.665066667 0.151966667 0.142233333 0.025933333 −0.017033333 BG065754 −0.2817 −0.338066667 0.0641 −0.269166667 −0.0909 BB781615 0.007033333 −0.750133333 −0.293733333 −0.302466667 −0.006366667 BC013893 0.487133333 −1.496 −0.5709 −0.5246 −0.165433333 BC003284 −0.239466667 −0.587633333 −0.210833333 −0.3991 0.09 BC006713 0.2405 −0.7469 −0.130466667 0.141933333 −0.103533333 NM_011075 −0.578166667 −1.007866667 0.389133333 −0.8714 0.161766667 BB009155 −0.348033333 −0.6079 −0.4697 −0.644766667 0.006466667 BG967046 −0.155666667 −0.632366667 −0.256166667 −0.2263 −0.092566667 NM_030697 −0.0882 −0.3339 −0.6238 0.085966667 −0.0261 BB275142 0.091 −0.196266667 −0.287366667 −0.530666667 0.0112 AV246296 −0.355833333 −1.045766667 −0.1054 −0.449 −0.113566667 NM_013738 −0.373033333 −1.171233333 −0.3378 −0.4434 −0.304966667 NM_018881 −0.0599 −0.2965 0.083833333 0.266733333 −0.167866667 BM936480 −0.074166667 −0.411966667 −0.043233333 0.1682 0.0483 BM198879 −0.102566667 −0.421266667 −0.160833333 −0.138233333 −0.056166667 AK018383 −0.215766667 −0.500633333 −0.0203 −0.063433333 0.0679 AV254764 0.396 −0.449933333 −0.264966667 −0.091133333 0.024033333 BC021352 −0.719166667 −1.279566667 0.065633333 −1.142833333 0.472066667 BB027848 0.205366667 −1.954166667 −0.2609 −0.5634 −0.066066667 AK017734 0.148866667 −1.2326 −0.527466667 −0.223133333 0.0878 AF069954 0.199 −0.442866667 −0.384666667 −0.168 0.184133333 BB770528 −0.4836 −0.9985 −0.001733333 −0.5238 −0.018233333 NM_009897 −0.061033333 −0.019033333 0.099633333 0.246833333 0.111033333 AK007854 0.551333333 −0.141066667 −0.224233333 1.0645 0.059733333 BI966443 −0.017133333 0.2259 0.077233333 0.1621 0.133533333 NM_013929 −0.529133333 0.175266667 −0.006633333 0.092566667 0.033 BG076151 −0.0769 −0.657266667 −0.240966667 −0.7006 −0.188666667 AV251625 0.067233333 −0.1615 0.0423 −0.2405 0.038133333 AV219418 0.828066667 0.6295 0.153866667 0.7223 0.4492 NM_011316 0.582866667 −0.9295 −0.471733333 −0.248533333 0.063766667 NM_007980 −0.284766667 −1.581066667 0.177566667 1.050633333 −0.506433333 BB046347 0.187 −0.804466667 −0.107166667 0.374733333 0.1608 AF335325 −0.056666667 0.507133333 −0.0865 −0.3547 0.141766667 AK010738 0.0059 −0.156866667 −0.066866667 0.034566667 0.3608 NM_134188 −0.137266667 −0.440166667 0.238166667 0.577266667 −0.070666667 NM_008935 −0.212433333 −1.001666667 0.377866667 0.588066667 0.408166667 BB140436 0.2823 2.071 −0.072366667 0.455033333 0.286066667 NM_019738 −0.514266667 −1.132266667 −0.632566667 −0.7691 −0.9543 X62701 −0.146033333 0.7376 0.7542 0.842633333 0.154 AV141095 −0.4102 −0.105133333 −0.0311 −0.035733333 −0.172533333 AI747296 −0.263733333 −0.3288 −0.0394 −0.157233333 −0.000966667 BC005552 0.125466667 0.4521 0.305566667 0.365 0.134333333 BB458460 −0.179166667 0.164466667 0.007966667 −0.250466667 0.131733333 BG076333 0.4759 0.873133333 0.238266667 0.256933333 −0.053333333 AK019979 −0.424933333 0.040866667 0.045466667 −0.645633333 −0.215433333 AV095209 −0.034666667 0.6413 0.2956 0.0573 0.260166667 AV216768 −0.417933333 −0.004066667 0.415333333 0.4269 0.167866667 AV221299 0.454466667 0.7477 0.164933333 0.3941 0.1454 BQ174991 0.1864 0.401733333 0.4039 0.543166667 −0.1546 NM_013642 0.3967 1.007033333 0.328566667 0.7153 0.265866667 L21027 −0.5404 −0.030733333 0.3857 0.4391 0.2905 BB204486 −0.3875 −0.0837 0.3188 0.321133333 0.130466667 BC025169 0.928266667 0.371166667 −0.0788 0.176 0.022133333 BC026131 0.4079 0.212366667 0.0392 0.3886 −0.0852 BC010318 0.1986 0.1861 0.135333333 0.227366667 0.0565 BB730977 −0.7313 0.991966667 0.753233333 0.126966667 0.329766667 AA561726 −0.429733333 −0.08 0.319366667 0.347333333 0.173933333 BC012955 0.2863 0.105133333 −0.255433333 0.249966667 −0.304933333 BC004827 −0.247666667 −0.2247 0.375566667 0.383233333 −0.070433333 NM_007556 0.168533333 0.427366667 0.524666667 0.467266667 0.0638 NM_134147 0.6449 0.014333333 −0.220733333 −0.2048 −0.1757 AV173869 0.3109 0.1411 −0.005066667 −0.3593 −0.025566667 AF022072 0.040666667 0.606166667 0.5563 0.7799 0.0814 BC019379 −0.192566667 −0.5665 0.0072 −0.033133333 −0.155466667 AK010447 −0.050233333 0.595633333 −0.108333333 −0.127666667 0.062233333 BC017615 −0.3193 −1.535366667 0.068266667 −0.278633333 −0.4904 BB246912 0.5739 0.302566667 −0.1992 0.039433333 0.3325 AF000969 0.294433333 −0.820166667 −0.559933333 0.6762 −0.180933333 BG066491 −0.348866667 −0.8219 −0.061 −0.627766667 −0.177133333 AF055573 0.368566667 −0.197333333 −0.168666667 0.0039 −0.094566667 NM_053122 0.1396 −0.096466667 −0.045133333 −0.1158 −0.142033333

EXAMPLES Example 1 Materials Used

Dulbecco's modified Eagle's medium (DMEM), fetal calf serum (FCS), Hanks' calcium- and magnesium-free buffer, insulin and Trizol were obtained from Invitrogen (Breda, The Netherlands). Glucagon, hydrocortisone, collagenase type IV, Benzo(a)pyrene (BaP), Aflatoxin B1 (AFB1), 2-Acetylaminofluorene (2-AAF), Dimethylnitrosamine (DMN), Mitomycin C (MitC), o-Anthranilic acid (ANAC), 2-(Chloromethyl)pyridine.HCl (2-CP), 4-Nitro-o-phenylenediamine (4-NP), Quercetin (Q), 8-Hydroxyquinoline (8-HQ), Trypan blue, dimethylsulphoxide (DMSO), bovine serum albumin (BSA), 4′,6-diamidino-2-phenylindole (DAPI) and Tween-20 were purchased from Sigma-Aldrich (Zwijndrecht, The Netherlands). Triton X-100, NaCl, Na₂HPO₄.2H₂O and NaH₂PO₄ were obtained from Merck (Darmstadt, Germany) and paraformaldehyde from ICN biomedicals (Auroro, Ohio). Collagen Type I Rat Tail was obtained from BD BioSciences (Bedford, Mass.). The RNeasy minikit was obtained from Qiagen, Westburg B.V. (Leusden, The Netherlands). The 5×MegaScript T7 Kit was obtained from Ambion (Austin, Tex.). The GeneChip® Expression 3′-Amplification Two-Cycle cDNA Synthesis Kit and Reagents, the Hybridization, Wash and Stain Kit and the Mouse Genome 430 2.0 Arrays were purchased from Affymetrix (Santa Clara, Calif.).

Example 2 Animals

Permission for performing animal studies was obtained from the Animal Ethical Committee. Adult male C57/B6 mice (Charles River), weighing 20-25 g, were obtained from Charles River GmbH, Sulzfeld, Germany. This mouse strain was chosen because it is frequently used in toxicological and pharmacological investigations, and it is a common background for transgenic mouse strains. The animals were housed in macrolon cages with sawdust bedding at 22° C. and 50-60% humidity. The light cycle was 12 h light/12 h dark. Feed and tap water were available ad libitum.

Example 3 Isolation of Hepatocytes

Hepatocytes were isolated from adult male C57/B6 mice by a two-step collagenase perfusion method according to Seglen and Casciano (16, 17), with modifications as described before (18). Cell viability and yield were determined by trypan blue exclusion.

Example 4 Cell Culturing and Treatments

Cells with viability >85%, were cultured in a collagen-collagen sandwich formation as described before (18, 19, 20). Prior to treatment, primary cultures of mouse hepatocytes were allowed to recover for 40-42 h at 3° C. in a humidified chamber with 95%/5% air/CO₂ in serum-free culture medium supplemented with insulin 0.5 U/ml), glucagon (7 nanog/ml), hydrocortisone (7.5 microg/ml) and 2% penicillin/streptomycin (5000 μml penicillin; 5000 microM/ml streptomycin). Culture medium was refreshed every 24 h. After the recovery period, the culture medium was replaced by culture medium containing one of the selected ten compounds, or with vehicle control. Only non-cytotoxic doses were used for each compound, which were determined by the MTT assay (ca 80% viability) and are presented in Table 9. Cells were incubated for 24 or 48 h before being harvested for RNA isolation by adding Trizol reagent. Three independent replicate biological experiments with hepatocytes from different mice were conducted for each compound.

TABLE 9 Solvents and dose used for several true GTX and false GTX compounds. GTX GTX Solvent and in in Chemical dose (v/v %) Dose vitro vivo True GTX compounds Benzo(a)pyrene DMSO, 0.5%  30 μM + + Aflatoxin B1 DMSO, 0.5%  15 μM + + 2-Acetylaminofluorene DMSO, 0.5% 125 μM + + Dimethylnitrosamine  2 mM + + Mitomycin C Ethanol, 0.5%  5 μM + + False GTX compounds o-Anthranilic acid DMSO, 0.5%  2 mM + − 2- DMSO, 0.5% 125 μM + − (Chloromethyl)pyridine•HCl 4-Nitro-o-phenylenediamine DMSO, 0.5%  2 mM + − Quercetin DMSO, 0.5% 200 μM + − 8-Hydroxyquinoline Ethanol, 0.5% 150 μM + −

Example 5 RNA Isolation

Total RNA was isolated from cultured mouse hepatocytes using Trizol and by means of the RNeasy kit according to the manufacturer's protocol. RNA concentrations were measured by means of a spectrophotometer and the quality of each RNA preparation was determined by means of a bio-analyzer (Agilent Technologies, The Netherlands). Only samples with a good quality (clear 18S and 28S peaks and RIN>6) were used for hybridization. Extracted RNA was stored at −80° C. until further analysis.

Example 6 Gene Expression Analysis, Target Preparation and Hybridization

Targets were prepared according to the Affymetrix protocol. cRNA targets were hybridized according to the manufacturer's recommended procedures on high-density oligonucleotide gene chips (Affymetrix Mouse Genome 430 2.0 GeneChip arrays). The gene chips were washed and stained using an Affymetrix fluidics station and scanned by means of an Affymetrix GeneArray scanner.

A total of eighty-two GeneChips was run. Normalization quality controls, including scaling factors, average intensities, present calls, background intensities, noise, and raw Q values, were within acceptable limits for all chips. Hybridization controls BioB, BioC, BioD, and CreX, were identified on all chips and yielded the expected increases in intensities.

Example 7 Selection of Differentially Expressed Probe Sets; True Versus False GTX

Eighty-two datasets were obtained from this experiment. Raw data were imported into ArrayTrack (22, 23) and normalized using Robust Multi-array Average (RMA, integrated into ArrayTrack) (24).

Present-Marginal-Absent calls were used to identify and omit probe sets of poor quality (25). Subsequently, the remaining probe sets were logarithmically (base 2) transformed, corrected for vehicle control, and subjected to statistical analysis (24 h: 26100; 48 h: 26690; total: 27363). For each time point, probe sets were then selected for which expression was up- or down-regulated by at least one compound at a minimum of 1.2-fold in at least two out of three experiments with expressions altered in the same direction in all replicate and with a mean fold up- or down-regulated of 1.5 (26). The generated list with differentially expressed probe sets (log 2 ratios) was used for hierarchical clustering (HCA) and prediction analysis of microarray (PAM) (10776 probe sets at 24 h and 12180 probe sets at 48 h).

Example 8 Class Prediction and Functional Analysis; True Versus False GTX

The software tool “prediction analysis of microarray” (PAM) was used for discriminating true GTX compounds from false GTX compounds (27). PAM uses gene expression data to calculate the shrunken centroid for each class and identifies the specific genes that determine the centroid most. Based on the nearest shrunken centroid, PAM is also capable of predicting to which class an unknown sample belongs (27). Class prediction was performed after 24 h and 48 h of exposure.

For this analysis, the gene list with differentially expressed probe sets was used. For each exposure period, 3 sets of genes (classifiers) were generated by PAM, using all ten treatments, based on the smallest estimated misclassification error rate (generated by 10-fold cross-validation) and a >80% predicted test probability. This was done by using 2 experiments as training set and the third experiment for validation. This was done for all 3 possible combinations, each time leaving out another experiment. For each time point, the classifiers that were in common between the three training sets, were set as the final classifier set for that time point

Example 9 Selection of Differentially Expressed Genes: GTX vs Non-GTX

Ninety datasets were obtained from this experiment. Raw data were normalized using Robust Multi-array Average (RMA) (24), using the custom chip description files (CDFs) as described by de Leeuw et al (BMC.Res.Notes, 2008, 1: 66.). Of the hybrid probe-set definitions included in the custom annotation, only the 16331 probe sets selected according to Dai et al (Nucleic Acids Res., 2005, 33: e175.) and the 4648 Affymetrix probe sets corresponding to an Entrez Gene ID were used in further analysis, giving a total of 20979 probe sets.

Subsequently, the remaining probe sets were logarithmically (base 2) transformed, corrected for vehicle control, and subjected to statistical analysis. For each gene, a significant response was scored if both of the following criteria were met: (a) if the gene expression values for the replicate compound-exposed samples differed significantly from the vehicle-exposed samples with a t-test p-value <0.01; (b) if the average gene expression value for the replicate compound-exposed samples was at least twice that of the average vehicle-exposed samples. If none or only one of these criteria were met, no point was scored. These calculations were performed in the statistical package R. The four genes with the highest scores in the GTX group and no scores in the non-GTX group were set as the classifier set.

Example 10 Class Prediction and Functional Analysis: GTX vs Non-GTX

For this analysis, the same gene scoring system as described above was used for the four genes with the highest scores (1700007K13RIK, GAS2L3, SPC25, DDIT4L). Compounds were scored using these four genes and it was found that a positive score in at least one gene resulted in identification of GTX compounds and not for non-GTX compounds.

The validity of this approach was verified using a leave-one compound-out strategy, each time leaving out another compound, giving 80% prediction or better.

Example 11 Validation of Classifiers

For the purpose of validating the class discrimination models with the final classifier sets, gene expression data were generated for two additional true GTX compounds, phenacetin and DMBA, and for three False GTX compounds, cur, ethylacrylate and resorcinol and the vehicle control for exposure periods of 24 and 48 h. All the independent triplicate treatments of all compounds were classified correctly with a predicted test probability of 100% at both time points, with the exception of phenacetin, which is misclassified as a False GTX compound, only at 48 h (Table III below). This resulted in a positive prediction value of 100% for both time points and a negative prediction value of 89 and 80% for 24 and 48 h, respectively.

TABLE 10 Overview of the five extra compounds used in primary mouse hepatocyte exposure validation study for true and false GTX prediction Concen- Chemical Abbreviation CAS nr. tration Vehicle True GTX compounds Dimethylbenzanthracene DMBA 57-97-6 500 μM DMSO Phenacetin Phen 62-44-2  1.5 mM Ethanol False GTX compounds Curcumin Cur 458-37-7  80 μM DMSO Ethyl acrylate Ethylacrylate 140-88-5 500 μM Ethanol Resorcinol Resorcinol 108-46-3  2 mM Ethanol

TABLE III Validation of the class prediction model with five additional compounds Prediction 24 h 48 h Compound Genotoxic class Exp 1 Exp 2 Exp 3 Exp 1 Exp 2 Exp 3 DMBA GTX GTX GTX GTX GTX GTX GTX Phen GTX GTX GTX GTX FP-GTX FP-GTX FP-GTX Cur FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX Ethylacrylate FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX Resorcinol FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX FP-GTX The intersection of the classifiers from Table II, for each time point separately, was used for generating the classification model in PAM. The five new compounds were used for validating that model.

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1. An in vitro method for distinguishing between genotoxic and non-genotoxic compounds, the method comprising: determining the expression level of gene 1700007K13Rik in primary mouse hepatocytes exposed to a potentially genotoxic compound; comparing the expression level of gene 1700007K13Rik with an expression level of gene 1700007K13Rik in primary mouse hepatocytes not exposed to the potentially genotoxic compound; and classifying the potentially genotoxic compound as genotoxic if the expression level of gene 1700007K13Rik in primary mouse hepatocytes after exposure to the potentially genotoxic compound is increased at least two-fold in comparison with the expression level of gene 1700007K13Rik in primary mouse hepatocytes not exposed to the potentially genotoxic compound.
 2. The method according to claim 1, wherein determining the expression level of gene 1700007K13Rik is performed at two different points in time.
 3. The method according to claim 2, wherein the two different points in time are about 24 hours and about 48 hours after exposure to the potentially genotoxic compound.
 4. The method according to claim 1, wherein the expression value of gene 1700007K13Rik is measured in two or more independent samples.
 5. The method according to claim 1, wherein the expression data are compared by means of a supervised classification method.
 6. The method according to claim 5, wherein the supervised classification method is selected from the group consisting of Prediction Analysis of Microarray, support vector machines, k-nearest neighbours, RandomForest, diagonal linear discriminant analysis, classification and regression trees, probabilistic neural network and Weighted Voting.
 7. The method according to claim 1, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 8. The method according to claim 2, wherein the expression value of gene 1700007K13Rik is measured in two or more independent samples.
 9. The method according to claim 3, wherein the expression value of gene 1700007K13Rik is measured in two or more independent samples.
 10. The method according to claim 2, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 11. The method according to claim 3, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 12. The method according to claim 4, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 13. The method according to claim 5, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 14. The method according to claim 6, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 15. The method according to claim 7, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 16. The method according to claim 8, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 17. The method according to claim 9, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 18. The method according to claim 9, wherein, in addition to gene 1700007K13Rik, the expression level of at least one gene selected from the group consisting of gene GAS2L3 (237436), gene SPC25 (66442) and gene DDIT4L (73284) is determined.
 19. The method according to claim 5, wherein the expression value of gene 1700007K13Rik is measured in two or more independent samples.
 20. The method according to claim 7, wherein the expression value of gene 1700007K13Rik is measured in two or more independent samples. 